NotebookLM Research Assistant Skill

Interact with Google NotebookLM to query documentation with Gemini's source-grounded answers. Each question opens a fresh browser session, retrieves the answer exclusively from your uploaded documents, and closes.

When to Use This Skill

Trigger when user:

• Mentions NotebookLM explicitly

• Shares NotebookLM URL (https://notebooklm.google.com/notebook/... )

• Asks to query their notebooks/documentation

• Wants to add documentation to NotebookLM library

• Uses phrases like "ask my NotebookLM", "check my docs", "query my notebook"

⚠️ CRITICAL: Add Command - Smart Discovery

When user wants to add a notebook without providing details:

SMART ADD (Recommended): Query the notebook first to discover its content:

Step 1: Query the notebook about its content

python scripts/run.py ask_question.py --question "What is the content of this notebook? What topics are covered? Provide a complete overview briefly and concisely" --notebook-url "[URL]"

Step 2: Use the discovered information to add it

python scripts/run.py notebook_manager.py add --url "[URL]" --name "[Based on content]" --description "[Based on content]" --topics "[Based on content]"

MANUAL ADD: If user provides all details:

• --url

• The NotebookLM URL

• --name

• A descriptive name

• --description

• What the notebook contains (REQUIRED!)

• --topics

• Comma-separated topics (REQUIRED!)

NEVER guess or use generic descriptions! If details missing, use Smart Add to discover them.

Critical: Always Use run.py Wrapper

NEVER call scripts directly. ALWAYS use python scripts/run.py [script] :

✅ CORRECT - Always use run.py:

python scripts/run.py auth_manager.py status python scripts/run.py notebook_manager.py list python scripts/run.py ask_question.py --question "..."

❌ WRONG - Never call directly:

python scripts/auth_manager.py status # Fails without venv!

The run.py wrapper automatically:

• Creates .venv if needed

• Installs all dependencies

• Activates environment

• Executes script properly

Core Workflow

Step 1: Check Authentication Status

python scripts/run.py auth_manager.py status

If not authenticated, proceed to setup.

Step 2: Authenticate (One-Time Setup)

Browser MUST be visible for manual Google login

python scripts/run.py auth_manager.py setup

Important:

• Browser is VISIBLE for authentication

• Browser window opens automatically

• User must manually log in to Google

• Tell user: "A browser window will open for Google login"

Step 3: Manage Notebook Library

List all notebooks

python scripts/run.py notebook_manager.py list

BEFORE ADDING: Ask user for metadata if unknown!

"What does this notebook contain?"

"What topics should I tag it with?"

Add notebook to library (ALL parameters are REQUIRED!)

python scripts/run.py notebook_manager.py add
--url "https://notebooklm.google.com/notebook/..."
--name "Descriptive Name"
--description "What this notebook contains" \ # REQUIRED - ASK USER IF UNKNOWN! --topics "topic1,topic2,topic3" # REQUIRED - ASK USER IF UNKNOWN!

Search notebooks by topic

python scripts/run.py notebook_manager.py search --query "keyword"

Set active notebook

python scripts/run.py notebook_manager.py activate --id notebook-id

Remove notebook

python scripts/run.py notebook_manager.py remove --id notebook-id

Quick Workflow

• Check library: python scripts/run.py notebook_manager.py list

• Ask question: python scripts/run.py ask_question.py --question "..." --notebook-id ID

Step 4: Ask Questions

Basic query (uses active notebook if set)

python scripts/run.py ask_question.py --question "Your question here"

Query specific notebook

python scripts/run.py ask_question.py --question "..." --notebook-id notebook-id

Query with notebook URL directly

python scripts/run.py ask_question.py --question "..." --notebook-url "https://..."

Show browser for debugging

python scripts/run.py ask_question.py --question "..." --show-browser

Follow-Up Mechanism (CRITICAL)

Every NotebookLM answer ends with: "EXTREMELY IMPORTANT: Is that ALL you need to know?"

Required Claude Behavior:

• STOP - Do not immediately respond to user

• ANALYZE - Compare answer to user's original request

• IDENTIFY GAPS - Determine if more information needed

• ASK FOLLOW-UP - If gaps exist, immediately ask: python scripts/run.py ask_question.py --question "Follow-up with context..."

• REPEAT - Continue until information is complete

• SYNTHESIZE - Combine all answers before responding to user

Script Reference

Authentication Management (auth_manager.py )

python scripts/run.py auth_manager.py setup # Initial setup (browser visible) python scripts/run.py auth_manager.py status # Check authentication python scripts/run.py auth_manager.py reauth # Re-authenticate (browser visible) python scripts/run.py auth_manager.py clear # Clear authentication

Notebook Management (notebook_manager.py )

python scripts/run.py notebook_manager.py add --url URL --name NAME --description DESC --topics TOPICS python scripts/run.py notebook_manager.py list python scripts/run.py notebook_manager.py search --query QUERY python scripts/run.py notebook_manager.py activate --id ID python scripts/run.py notebook_manager.py remove --id ID python scripts/run.py notebook_manager.py stats

Question Interface (ask_question.py )

python scripts/run.py ask_question.py --question "..." [--notebook-id ID] [--notebook-url URL] [--show-browser]

Data Cleanup (cleanup_manager.py )

python scripts/run.py cleanup_manager.py # Preview cleanup python scripts/run.py cleanup_manager.py --confirm # Execute cleanup python scripts/run.py cleanup_manager.py --preserve-library # Keep notebooks

Environment Management

The virtual environment is automatically managed:

• First run creates .venv automatically

• Dependencies install automatically

• Chromium browser installs automatically

• Everything isolated in skill directory

Manual setup (only if automatic fails):

python -m venv .venv source .venv/bin/activate # Linux/Mac pip install -r requirements.txt python -m patchright install chromium

Data Storage

All data stored in ~/.claude/skills/notebooklm/data/ :

• library.json

• Notebook metadata

• auth_info.json

• Authentication status

• browser_state/

• Browser cookies and session

Security: Protected by .gitignore , never commit to git.

Configuration

Optional .env file in skill directory:

HEADLESS=false # Browser visibility SHOW_BROWSER=false # Default browser display STEALTH_ENABLED=true # Human-like behavior TYPING_WPM_MIN=160 # Typing speed TYPING_WPM_MAX=240 DEFAULT_NOTEBOOK_ID= # Default notebook

Decision Flow

User mentions NotebookLM ↓ Check auth → python scripts/run.py auth_manager.py status ↓ If not authenticated → python scripts/run.py auth_manager.py setup ↓ Check/Add notebook → python scripts/run.py notebook_manager.py list/add (with --description) ↓ Activate notebook → python scripts/run.py notebook_manager.py activate --id ID ↓ Ask question → python scripts/run.py ask_question.py --question "..." ↓ See "Is that ALL you need?" → Ask follow-ups until complete ↓ Synthesize and respond to user

Troubleshooting

Problem Solution

ModuleNotFoundError Use run.py wrapper

Authentication fails Browser must be visible for setup! --show-browser

Rate limit (50/day) Wait or switch Google account

Browser crashes python scripts/run.py cleanup_manager.py --preserve-library

Notebook not found Check with notebook_manager.py list

Best Practices

• Always use run.py - Handles environment automatically

• Check auth first - Before any operations

• Follow-up questions - Don't stop at first answer

• Browser visible for auth - Required for manual login

• Include context - Each question is independent

• Synthesize answers - Combine multiple responses

Limitations

• No session persistence (each question = new browser)

• Rate limits on free Google accounts (50 queries/day)

• Manual upload required (user must add docs to NotebookLM)

• Browser overhead (few seconds per question)

Resources (Skill Structure)

Important directories and files:

• scripts/

• All automation scripts (ask_question.py, notebook_manager.py, etc.)

• data/

• Local storage for authentication and notebook library

• references/

• Extended documentation:

• api_reference.md

• Detailed API documentation for all scripts

• troubleshooting.md

• Common issues and solutions

• usage_patterns.md

• Best practices and workflow examples

• .venv/

• Isolated Python environment (auto-created on first run)

• .gitignore

• Protects sensitive data from being committed